Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation

TL;DR

This paper presents a quantum minimal learning machine (QMLM), a similarity-based supervised learning algorithm adapted for quantum data, used for error mitigation in quantum computing.

Contribution

It introduces the QMLM, a novel quantum learning model inspired by classical methods, specifically designed for quantum data and error mitigation.

Findings

QMLM effectively mitigates errors in quantum data.

The approach demonstrates promising results across different quantum parameters.

QMLM bridges classical and quantum machine learning techniques.

Abstract

We introduce the concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm. The algorithm is conceptually based on a classical machine learning model and adopted to work with quantum data. We will motivate the theory and run the model as an error mitigation method for various parameters.